scholarly journals West Lake Tourist: A Visual Analysis System Based on Taxi Data

Smart Cities ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 345-358 ◽  
Author(s):  
Jiang ◽  
Cao ◽  
Liu ◽  
Fan
Keyword(s):  
Domain Knowledge ◽  
Visual Analysis ◽  
Route Planning ◽  
Trajectory Data ◽  
West Lake ◽  
Point Of Interest ◽  
Scenic Spot ◽  
City Construction ◽  
Analysis System

Mining the mobile pattern of the urban population plays an important role in city construction, and visual analysis is a powerful technique in studying mobile patterns. In this paper, based on the taxi trajectory data in Hangzhou, we share our design for an interactive visual analytic system, which helps analyzers leverage their domain knowledge to gain insight into travel patterns, including travel time rules of tourists and the distribution rules of pick-up and drop-off locations. Besides, our system can present the dynamic travel process and the Point of Interest (POIs) information of the origin and the destination. A case study has been conducted, which verifies that our system can provide tools for urban managers or urban experts on the design of scenic spot open entrances and exits and travel route planning.

A prediction model of vessel trajectory based on generative adversarial network

2021 ◽  
pp. 1-11
Author(s):  
Senjie Wang ◽  
Zhengwei He
Keyword(s):  
Safety Management ◽  
Route Planning ◽  
Local Area ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Ship Traffic ◽  
Adversarial Network ◽  
Historical Trajectory ◽  
Vessel Motion

Abstract Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

scholarly journals Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou

2021 ◽  
Vol 13 (2) ◽  
pp. 444
Author(s):  
Xucai Zhang ◽  
Yeran Sun ◽  
Ting On Chan ◽  
Ying Huang ◽  
Anyao Zheng ◽  
...  
Keyword(s):  
Rapid Development ◽  
Ordinary Least Squares ◽  
Information And Communications Technology ◽  
Time Data ◽  
Location Data ◽  
Point Of Interest ◽  
Quality Life ◽  
City Construction ◽  
The Impact ◽  
The City

Urban vibrancy contributes towards a successful city and high-quality life for people as one of its vital elements. Therefore, the association between service facilities and vibrancy is crucial for urban managers to understand and improve city construction. Moreover, the rapid development of information and communications technology (ICT) allows researchers to easily and quickly collect a large volume of real-time data generated by people in daily life. In this study, against the background of emerging multi-source big data, we utilized Tencent location data as a proxy for 24-h vibrancy and adopted point-of-interest (POI) data to represent service facilities. An analysis framework integrated with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models is proposed to explore the spatiotemporal relationships between urban vibrancy and POI-based variables. Empirical results show that (1) spatiotemporal variations exist in the impact of service facilities on urban vibrancy across Guangzhou, China; and (2) GTWR models exhibit a higher degree of explanatory capacity on vibrancy than the OLS models. In addition, our results can assist urban planners to understand spatiotemporal patterns of urban vibrancy in a refined resolution, and to optimize the resource allocation and functional configuration of the city.

scholarly journals Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

2021 ◽  
pp. 036119812199550
Author(s):  
Danyang Sun ◽  
Fabien Leurent ◽  
Xiaoyan Xie
Keyword(s):  
Large Scale ◽  
City Planning ◽  
Human Mobility ◽  
Work Place ◽  
Trajectory Data ◽  
Floating Car Data ◽  
Vehicle Trajectories ◽  
Activity Types ◽  
Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

scholarly journals Research on the Method of Urban Jobs-Housing Space Recognition Combining Trajectory and POI Data

2021 ◽  
Vol 10 (2) ◽  
pp. 71
Author(s):  
Ya Zhang ◽  
Jiping Liu ◽  
Yong Wang ◽  
Yungang Cao ◽  
Youda Bai
Keyword(s):  
Flow Model ◽  
Trajectory Data ◽  
Dynamic Flow ◽  
Public Facilities ◽  
Point Of Interest ◽  
Recognition Result ◽  
Different Types ◽  
Core Issue ◽  
Space Recognition ◽  
New Framework

With the gradual emergence of the separation and dislocation of urban jobs-housing space, rational planning of urban jobs-housing space has become the core issue of national land-spatial planning. To study the existing relationship between workspaces and living spaces, a new method to identify jobs-housing space is proposed, which not only considers the static spatial distribution of urban public facilities but also identifies the jobs-housing space by analyzing the real mobility characteristics of people from a humanistic perspective. This method provides a new framework for the identification of urban jobs-housing space by integrating point-of-interest (POI) and trajectory data. The method involves three steps: Firstly, based on the trajectory data, we analyze the characteristics of the dynamic flow of passengers in the grid and construct the living factors and working factors to identify the distribution of jobs-housing space. Secondly, we reclassify the POIs to calculate the category ratios of different types of POIs in the grid to identify the jobs-housing space. Finally, an OR operation is performed on the results obtained by the two methods to obtain the final recognition result. We selected Haikou City as the experimental area to verify the method proposed in this paper. The experimental results show that the recognition accuracy of the travel flow model is 72.43%, the POI quantitative recognition method’s accuracy is 74.94%, and the accuracy of the method proposed in this paper is 85.90%, which is significantly higher than the accuracy of the previous two methods. Therefore, the method proposed here can serve as a reference for subsequent research on urban jobs-housing space.

scholarly journals The Optimization of Alternating Current Electrospun PA 6 Solutions Using a Visual Analysis System

Polymers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 2098
Author(s):  
Tomas Kalous ◽  
Pavel Holec ◽  
Jakub Erben ◽  
Martin Bilek ◽  
Ondrej Batka ◽  
...  
Keyword(s):  
High Voltage ◽  
High Speed ◽  
Visual Analysis ◽  
Polyamide 6 ◽  
Alternating Current ◽  
Sine Wave ◽  
Electrospinning Process ◽  
Camera System ◽  
Polymeric Solutions ◽  
Analysis System

The electrospinning process that produces fine nanofibrous materials have a major disadvantage in the area of productivity. However, alternating current (AC) electrospinning might help to solve the problem via the modification of high voltage signal. The aforementioned productivity aspect can be observed via a camera system that focuses on the jet creation area and that measures the average lifespan. The paper describes the optimization of polyamide 6 (PA 6) solutions and demonstrates the change in the behavior of the process following the addition of a minor dose of oxoacid. This addition served to convert the previously unspinnable (using AC) solution to a high-quality electrospinning solution. The visual analysis of the AC electrospinning of polymeric solutions using a high-speed camera and a programmable power source was chosen as the method for the evaluation of the quality of the process. The solutions were exposed to high voltage applying two types of AC signal, i.e., the sine wave and the step change. All the recordings presented in the paper contained two sets of data: firstly, camera recordings that showed the visual expression of electrospinning and, secondly, signal recordings that provided information on the data position in the signal function.

Narrative Scientific Data Visualization in an Immersive Environment

2021 ◽  
Author(s):  
Richen Liu ◽  
Hailong Wang ◽  
Chuyu Zhang ◽  
Xiaojian Chen ◽  
Lijun Wang ◽  
...  
Keyword(s):  
Data Visualization ◽  
Domain Knowledge ◽  
User Study ◽  
Scientific Visualization ◽  
Scientific Data ◽  
Immersive Environments ◽  
Related Information ◽  
New Strategy ◽  
Scientific Data Visualization

Abstract Motivation Narrative visualization for scientific data explorations can help users better understand the domain knowledge, because narrative visualizations often present a sequence of facts and observations linked together by a unifying theme or argument. Narrative visualization in immersive environments can provide users with an intuitive experience to interactively explore the scientific data, because immersive environments provide a brand new strategy for interactive scientific data visualization and exploration. However, it is challenging to develop narrative scientific visualization in immersive environments. In this paper, we propose an immersive narrative visualization tool to create and customize scientific data explorations for ordinary users with little knowledge about programming on scientific visualization, They are allowed to define POIs (point of interests) conveniently by the handler of an immersive device. Results Automatic exploration animations with narrative annotations can be generated by the gradual transitions between consecutive POI pairs. Besides, interactive slicing can be also controlled by device handler. Evaluations including user study and case study are designed and conducted to show the usability and effectiveness of the proposed tool. Availability Related information can be accessed at: https://dabigtou.github.io/richenliu/

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